PRODUCT RECOMMENDATION SYSTEM USING WORD2VEC

Abstract

In the era of e-commerce, personalized product recommendation systems play a crucial role in enhancing user experience and driving sales. This paper explores the application of Word2Vec, a popular word embedding technique, to develop a product recommendation system. Word2Vec is employed to generate vector representations of product descriptions, enabling the system to capture semantic relationships between products based on their textual content. By leveraging these word embeddings, we propose a recommendation approach that measures the similarity between products, allowing for more accurate and context-aware suggestions. Experimental results show that the proposed method outperforms traditional content-based recommendation systems in terms of relevance and diversity, offering a promising approach to enhance recommendation quality in online retail environments. Furthermore, the paper discusses the challenges and limitations of using Word2Vec for product recommendation and provides insights for future research in the field.